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Swain, P. H.

Publications and source records attributed to Swain, P. H..

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Selective radiant temperature mapping using a layered classifier

A method of measuring temperatures of selected ground-cover types using remotely sensed multispectral scanner data and a layered classification approach is described. A brief review of radiation theory is presented to show that for the wavelength bands and temperature ranges involved in remote sensing applications, a linear calibration function can be satisfactorily utilized. An example of the application of the layered classifier for temperature mapping of water is shown.

Bartolucci, L. A.↗
NASA NTRS1975

Crop identification technology assessment for remote sensing (CITARS). Volume 6: Data processing at the laboratory for applications of remote sensing

The results of classifications and experiments for the crop identification technology assessment for remote sensing are summarized. Using two analysis procedures, 15 data sets were classified. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. Additionally, 20 data sets were classified using training statistics from another segment or date. The classification and proportion estimation results of the local and nonlocal classifications are reported. Data also describe several other experiments to provide additional understanding of the results of the crop identification technology assessment for remote sensing. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, spectral discriminability of corn, soybeans, and other, and analyses of aircraft multispectral data.

Bauer, M. E.↗
NASA NTRS1975

Layered classification techniques for remote sensing applications

The single-stage method of pattern classification utilizes all available features in a single test which assigns the unknown to a category according to a specific decision strategy (such as the maximum likelihood strategy). The layered classifier classifies the unknown through a sequence of tests, each of which may be dependent on the outcome of previous tests. Although the layered classifier was originally investigated as a means of improving classification accuracy and efficiency, it was found that in the context of remote sensing data analysis, other advantages also accrue due to many of the special characteristics of both the data and the applications pursued. The layered classifier method and several of the diverse applications of this approach are discussed.

Swain, P. H.↗
NASA NTRS1975

The decision tree approach to classification

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

Wu, C.↗
NASA NTRS1975

Crop Identification Technology Assessment for Remote Sensing (CITARS)

The results of classifications and experiments performed for the Crop Identification Technology Assessment for Remote Sensing (CITARS) project are summarized. Fifteen data sets were classified using two analysis procedures. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. In addition, 20 data sets were classified using training statistics from another segment or date. The results of both the local and non-local classifications in terms of classification and proportion estimation are presented. Several additional experiments are described which were performed to provide additional understanding of the CITARS results. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, the spectral discriminability of corn, soybeans, and other, and analysis of aircraft multispectral data.

Bauer, M. E.↗
NASA NTRS1975

Remote terminal system evaluation

An Earth Resources Data Processing System was developed to evaluate the system for training, technology transfer, and data processing. In addition to the five sites included in this project two other sites were connected to the system under separate agreements. The experience of these two sites is discussed. The results of the remote terminal project are documented in seven reports: one from each of the five project sites, Purdue University, and an overview report summarizing the other six reports.

Phillips, T. L.↗
NASA NTRS1975

The decision tree classifier - Design and potential

A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

Hauska, H.↗
NASA NTRS1974

Implementation and evaluation of ILLIAC 4 algorithms for multispectral image processing

Data concerning a multidisciplinary and multi-organizational effort to implement multispectral data analysis algorithms on a revolutionary computer, the Illiac 4, are reported. The effectiveness and efficiency of implementing the digital multispectral data analysis techniques for producing useful land use classifications from satellite collected data were demonstrated.

Swain, P. H.↗
NASA NTRS1974

Implementation of ILLIAC 4 algorithms for multispectral image interpretation

Research has focused on the design and partial implementation of a comprehensive ILLIAC software system for computer-assisted interpretation of multispectral earth resources data such as that now collected by the Earth Resources Technology Satellite. Research suggests generally that the ILLIAC 4 should be as much as two orders of magnitude more cost effective than serial processing computers for digital interpretation of ERTS imagery via multivariate statistical classification techniques. The potential of the ARPA Network as a mechanism for interfacing geographically-dispersed users to an ILLIAC 4 image processing facility is discussed.

Ray, R. M.↗
NASA NTRS1974

An automated and repeatable data analysis procedure for remote sensing applications

A new multispectral data analysis procedure, based on LARSYS, has been developed which substantially reduces the influence of the analyst. The analysis is automated, including the interpretation of clustering results. The classification results obtained are repeatable and not biased by analyst subjectivity during the analysis.

Davis, B. J.↗
NASA NTRS1973

Pattern recognition: A basis for remote sensing data analysis

The theoretical basis for the pattern-recognition-oriented algorithms used in the multispectral data analysis software system is discussed. A model of a general pattern recognition system is presented. The receptor or sensor is usually a multispectral scanner. For each ground resolution element the receptor produces n numbers or measurements corresponding to the n channels of the scanner.

Swain, P. H.↗
NASA NTRS1973

The role of computer networks in remote sensing data analysis

It has been hypothesized that computer networks can be used to make data processing facilities available to the remote sensing community both quickly and effectively. An experiment to test this hypothesis is being conducted by the Laboratory for Applications of Remote Sensing at Purdue University, with the participation of potential users at several remote sites. Initial indications have been highly favorable, although final evaluation awaits further experience and the accumulation of usage data.

Swain, P. H.↗
NASA NTRS1973

Two effective feature selection criteria for multispectral remote sensing

Distance measures which are useful for feature selection are considered, giving attention to the divergence distance measure and the Jeffreys-Matusita (JM) distance. Experimental studies show that the JM-distance yields more reliable results than other distance measures. A number of questions which are not solved by the experiments are discussed. The investigation provides an explanation for previous observations that the JM-distance and a saturating transform of divergence are highly useful for feature selection in the multiclass case.

Swain, P. H.↗
NASA NTRS1972

Data processing 1: Advancements in machine analysis of multispectral data

Multispectral data processing procedures are outlined beginning with the data display process used to accomplish data editing and proceeding through clustering, feature selection criterion for error probability estimation, and sample clustering and sample classification. The effective utilization of large quantities of remote sensing data by formulating a three stage sampling model for evaluation of crop acreage estimates represents an improvement in determining the cost benefit relationship associated with remote sensing technology.

Swain, P. H.↗
NASA NTRS1972

Determining density of maize canopy. 1: Digitized photography

The relationship between different densities of maize (Zea mays L.) canopies and the energy reflected by these canopies was studied. Field plots were laid out, representing four growth stages of maize, on a dark soil and on a very light colored surface soil. Spectral and spatial data were obtained from color and color infrared photography taken from a vertical distance of 10 m above the maize canopies. Estimates of ground cover were related to field measurements of leaf area index. Ground cover was predicted from leaf area index measurements by a second order equation. Color infrared photography proved helpful in determining the density of maize canopy on dark soils. Color photography was useful for determining canopy density on light colored soils. The near infrared dye layer is the most valuable in canopy density determinations.

Stoner, E. R.↗
NASA NTRS1972

Definition of spectrally separable classes for soil survey research

A procedure is outlined for defining spectral classes such that the differences between classes can be quantified. It also facilitates determination of a number of classes such that the classes are spectrally discriminable. This is accomplished by partitioning the data into many classes and then combining similar spectral classes on the basis of appropriate criteria. Multispectral data were collected over a 12-mile flightline in White County, Indiana, in connection with the 1971 Corn Blight Watch Experiment. Data were collected in May by the University of Michigan airborne scanning spectrometer at an altitude of 5000 feet. Spectral maps resulting from the analysis were compared to existing soil surveys of the National Cooperative Soil Survey. The method should help determine the extent to which spectral properties of soil surfaces can be associated with morphologic and topographic differences of interest to soil surveyors engaged in operational soil mapping.

Cipra, J. E.↗